Remote Sensing Evaluation of Environmental Factors for Disease Prediction by RS-GIS

Public health is still seriously threatened by infectious illnesses, necessitating prompt, efficient surveillance and control methods. In recent years, monitoring environmental factors that affect disease transmission has shown significant promise when remote sensing technologies are combined with Geographic Information Systems (GIS). Through the examination of environmental variables like temperature, humidity, and vegetation, this research the paper seeks to evaluate the application of satellite images and GIS data for early disease prediction. By utilizing advanced image processing techniques and statistical models, remote sensing has enabled the identification of disease hotspots and the prediction of the spatial distribution of diseases. By analyzing and integrating these remote sensing data with disease incidence data, researchers have been able to develop models and predictive tools that help in the identification and forecasting of disease outbreaks. Additionally, the integration of remote sensing data with Enhanced Brownian Motion based on Dragonfly Algorithm has further enhanced the accuracy and reliability of disease prediction models. This research helps to establish proactive, data-driven public health initiatives by revealing the connection between environmental factors and disease outbreaks.